Search Results for author: Jiani Zhang

Found 13 papers, 7 papers with code

Large Language Models on Tabular Data -- A Survey

no code implementations27 Feb 2024 Xi Fang, Weijie Xu, Fiona Anting Tan, Jiani Zhang, Ziqing Hu, Yanjun Qi, Scott Nickleach, Diego Socolinsky, Srinivasan Sengamedu, Christos Faloutsos

Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding.

OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

no code implementations22 Feb 2024 Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, George Karypis

Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously.

Retrieval

NameGuess: Column Name Expansion for Tabular Data

1 code implementation19 Oct 2023 Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Shen Wang, Huzefa Rangwala, George Karypis

Recent advances in large language models have revolutionized many sectors, including the database industry.

Text Generation

OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization

no code implementations31 Jan 2023 Hengrui Zhang, Shen Wang, Vassilis N. Ioannidis, Soji Adeshina, Jiani Zhang, Xiao Qin, Christos Faloutsos, Da Zheng, George Karypis, Philip S. Yu

Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.

Node Classification

MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

2 code implementations5 Feb 2020 Xinyu Fu, Jiani Zhang, Ziqiao Meng, Irwin King

A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types.

Clustering Graph Embedding +3

STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems

no code implementations27 May 2019 Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King

We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario.

Link Prediction Matrix Completion +1

Title-Guided Encoding for Keyphrase Generation

no code implementations26 Aug 2018 Wang Chen, Yifan Gao, Jiani Zhang, Irwin King, Michael R. Lyu

Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP).

Keyphrase Generation

Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network

no code implementations CIKM 2017 Jiajun Cheng, Shenglin Zhao, Jiani Zhang, Irwin King, Xin Zhang, Hui Wang

However, the prior work only attends to the sentiment information and ignores the aspect-related information in the text, which may cause mismatching between the sentiment words and the aspects when an unrelated sentiment word is semantically meaningful for the given aspect.

Classification Sentence +2

Dynamic Key-Value Memory Networks for Knowledge Tracing

1 code implementation24 Nov 2016 Jiani Zhang, Xingjian Shi, Irwin King, Dit-yan Yeung

Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities.

Knowledge Tracing

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